CS 124 Python Tutorial - Stanford University(cs124-env) kpatel$ python3 my_script.py (cs124-env)...

CS 124 Python TutorialJanuary 14, 2019

Krishna Patel [email protected]

www.tinyurl.com/cs124python

Running Python

(cs124-env) kpatel$

Interactive Interpreter

You can write Python code right here!

Python 3.5.2 (default, Nov 12 2018, 13:43:14) [GCC 5.4.0 20160609] on linux Type "help", "copyright", "credits" or "license" for more information. >>>

python3

Based on a slide by Sam Redmond

(cs124-env) kpatel$ python3 my_script.py <output from the script>

(cs124-env) kpatel$ python3 hello.py What is your name? Krishna Hey Krishna, I'm Python!

Running Python Scripts

Supply the filename of the Python script to run after the python3 command


Python Basics

# Single line comments start with a '#'

""" Multiline comments can be written between three "s and are often used as function and module comments. """

Comments


3 # => 3 (int) 3.0 # => 3.0 (float)

1 + 1 # => 2 8 - 1 # => 7 10 * 2 # => 20 5 / 2 # => 2.5 9 / 3 # => 3.0 7 / 1.4 # => 5.0

7 // 3 # => 2 (integer division) 7 % 3 # => 1 (integer modulus) 2 ** 4 # => 16 (exponentiation)

Numbers and Math

Python has two numeric types int and float


# Unicode by default greeting = 'Hello' group = "wørld"

# Concatenate combo = greeting + ' ' + group + "!"

combo == "Hello wørld!" # => True

Strings

No char in Python! Both ' and " make string literals


True # => True False # => False

not True # => False True and False # => False True or False # => True (short-circuits)

1 == 1 # => True 2 * 3 == 5 # => False 1 != 1 # => False 2 * 3 != 5 # => True

1 < 10 # => True 2 >= 0 # => True 1 < 2 < 3 # => True (1 < 2 and 2 < 3) 1 < 2 >= 3 # => False (1 < 2 and 2 >= 3)

Booleansbool is a subtype of int, where True == 1 and False == 0


Similar to null in other languages

not None # => True bool(None) vv # => False

None

None has an inherent value of “False”

if the_world_is_flat: print("Don't fall off!")

If Statements

No parentheses needed Colon

No curly braces!

Use 1 tab or 4 spaces for indentation — be

consistent Based on a slide by Sam Redmond

Exercise 1

elif and else

zero or more elifs

else is optional

Python has no switch statement, opting for if/elif/else chains

if name == "jurafsky": print("dan is lecturing") elif name == "kpatel": print("krishna is lecturing") else: print("someone else is lecturing")


my_integer = 10 # => create an integer my_string = ‘hello!’ v# => create a string

my_integer = my_string # => set my_integer to my_string print(my_integer) # => prints ‘hello!’

Variables

There are no types in Python!

For Loops

Strings, lists, etc.

No loop counter!

for item in iterable: process(item)


Looping over Collections# Loop over words in a list. for color in ["red", "green", "blue", "yellow"]: print(color)

# => "red" # => "green" # => "blue" # => "yellow"


Looping with an Index

for idx in range(3): print(idx)

# => 0 # => 1 # => 2

for idx in range(3, 6): print(idx)

# => 3 # => 4 # => 5

Exercise 2

While Loops

while condition: # do something

break and continuei = 0 while True: print(i) if i == 2: break i += 1

# => 0 # => 1 # => 2

i = 0 while i <= 3: i += 1 if i == 1: continue print(i)

# => 2 # => 3 # => 4

Writing FunctionsThe def keyword defines a function Parameters have no explicit types

return is optional if either return or its value are omitted,

implicitly returns None

def fn_name(param1, param2): ... return value_1, value_2

You can return multiple values


Data Structures

List

Square brackets delimit lists

Commas separate elements

easy_as = [1,2,3]


Inspecting List Elementsempty = [] also_empty = list() letters = ['a', 'b', 'c', 'd'] numbers = [2, 3, 5, 7, 11]

# Access elements at a particular index numbers[0] # => 2 numbers[-1] # => 11

# You can also slice lists - the same rules apply letters[:3] # => ['a', 'b', 'c'] numbers[2:-1] # => [5, 7]


Exercises 3, 4

Useful List Functions

# Add to end of list lst.append(2) # => [2] lst.append(50) # => [2, 50]

# Insert at position lst.insert(0, ‘hi’) # => ['hi', 2, 50]

# Remove from position lst.pop(1) # => [‘hi’, 50]

Find more functions here: https://docs.python.org/3/tutorial/datastructures.html

Length

numbers = [2, 3, 5, 7, 11] hello = “hi!”

# find the length len(numbers) # => 5 len(hello) # => 3

Counting vs. Indexing

0 1 2 3 letters = ['a', 'b', 'c', ‘d’] len(letters) # => 4

Index

Setsempty = set() letters = ['a', ‘b', ‘a’] letters_set = set(letters) # => set(‘a’, ‘b’)

# Add to a set letters_set.add(‘c’) # => set(‘a’, ‘b’, ‘c’) letters_set.add(‘b’) # => set(‘a’, ‘b’, ‘c’)

# Membership ‘b’ in letters_set # => True ‘c’ in letters # => False

Dictionariesempty = {} also_empty = dict() d = {‘one’: 1, ‘two’: 2, ‘three’:3}

# Getting d[‘one’] # => 1 d[‘four’] # => Raises KeyError

# Setting d[‘one’] = 3 # => {‘one’: 3, ‘two’: 2, ‘three’:3} d[‘six’] = 4 # => {‘one’: 3, ‘two’: 2, ‘three’:3,‘six’:4}

# Membership (looks at keys) ‘one’ in d # => True 2 in d # => False

Keys must be unique

Exercise 5

Tuplesletters = ['a', 'b', 'c', 'd']

# Creating Tuples letter_tup = tuple(letters) # => (‘a’, ‘b’, ‘c’, ‘d’) num_tup = (1, 2)

# Indexing letter_tup[0] # => ’a’

# Cannot change contents once created letter_tup[0] = 4 # => Raises Error

# Membership ‘a’ in letter_tup # => True

tuples are immutable

import numpy as np

Arrays

# Array filled with zeros zeros = np.zeros(4) # => array([0., 0., 0., 0.])

# Array filled with ones ones = np.ones((2, 1)) # => array([[1.],

[1.]])

# Python List to Numpy Array arr = np.array([1, 3, 4]) # => array([1., 3., 4.])

Axis

np.ones((1, 2, 3)) # array([[[1., 1., 1.], [1., 1., 1.]]])

axis 0 (rows)

axis 1 (columns)

axis 2 (depth)

Arraysones = np.ones((2, 1)) # => array([[1.],

[1.]]) arr = np.array([1, 3, 4]) # => array([1., 3., 4.]) cat = np.array([3, 2]) # => array([3., 2.])

# Useful Tricks arr[1] = 9 # => array([1., 9., 4.]) len(ones) # => 2 ones.shape # => (2, 1) ones.reshape(2) # => array([[1., 1.]]) np.concatenate((arr, cat)) => array([1, 3, 4, 3, 2])

Useful Math# Natural log np.log(10) # => 2.302

# Squareroot np.sqrt(4) # => 2

# Dot Product a = np.array([1, 0]) b = np.array([2, 3]) a.dot(b) # => 2

# L2 Norm / Euclidian Distance np.linalg.norm(a) # => 1.0

Broadcastingrange = np.arange(4) # => array([0, 1, 2, 3])

# Vector and a Scalar range * 2 # => array([0, 2, 4, 6])

# Vector and a Vector range / range # => array([nan, 1., 1., 1.])

# Vector and a Matrix matrix = np.ones((3, 4)) range + matrix # => array([[ 1., 2., 3., 4.],

[ 1., 2., 3., 4.], [ 1., 2., 3., 4.]]) When you think of broadcasting, think of element-wise operations

Exercise 6

Documentation

The best resource!

https://docs.scipy.org/doc/numpy/reference/routines.html

Regular Expressions

re — Regular expression operations# Search for pattern match anywhere in string; return None if not found m = re.search(r"(\w+) (\w+)", "Isaac Newton, Physicist") m.group(0) # "Isaac Newton" - the entire match

m.group(1) # "Isaac" - first parenthesized subgroup m.group(2) # "Newton" - second parenthesized subgroup

# Match pattern against start of string; return None if not found

m = re.match(r"(?P<fname>\w+) (?P<lname>\w+)", "Malcolm Reynolds") m.group('fname') # => 'Malcolm' m.group('lname') # => 'Reynolds'


# Substitute occurrences of one pattern with another re.sub(r'@\w+\.com', '@stanford.edu', '[email protected] [email protected]')

# => [email protected] [email protected]

pattern = re.compile(r'[a-z]+[0-9]{3}') # compile pattern for fast ops match = re.search(pattern, '@@@abc123') # pattern is first argument

match.span() # (3, 9)

re — Regular expression operations


Exercise 7

https://www.learnpython.org/ on ChromeModules and Packages Numpy Arrays Generators List Comprehensions Regular Expressions Sets Decorators

Hello, World! Variables and Types Lists Basic Operators String Formatting Basic String Operations Conditions Loops Functions Dictionaries

Unix Videos (follow along)https://tinyurl.com/unix-videosVideos 1, 4, 6-10, 14-19


Sklearn

Documentation

The best resource!

https://scikit-learn.org/stable/user_guide.html

Linear Regression

from sklearn.linear_model import LinearRegression x = np.array([1, 2, 3, 4]) y = np.array([4, 5, 6, 7])

# Fit your linear regression reg = LinearRegression().fit(x, y)

# Predict reg.predict(np.array([1, 2]))

CS 124 Python Tutorial - Stanford University(cs124-env) kpatel$ python3 my_script.py (cs124-env)...

Documents

Transcript of CS 124 Python Tutorial - Stanford University(cs124-env) kpatel$ python3 my_script.py (cs124-env)...